A study of Monte Carlo Methods for Phantom Go

• Main Authors: Buron, Cedric, 卜賽德
• Other Authors: Wu, I-Chen
• Format: Others
• Language: en_US
• Published: 2013
• Online Access: http://ndltd.ncl.edu.tw/handle/22048040902855238985

碩士 === 國立交通大學 === 資訊科學與工程研究所 === 102 === This thesis deals with imperfect information games and the application of Monte Carlo methods to build an effective playing program. Games are an important field for testing Artificial Intelligence. Nowadays, the most efficient methods are Monte Carlo ones. These methods are based on probabilities, and have been widely used to create playing programs, particularly for the game of go, but also for imperfect information games, as Bridge, Poker or Phantom Go; phantom games are created according to a Perfect Information game, but in which each player only sees his own moves. Imperfect information games are quite hard to handle. As the different state of the game is unknown to the players, it is very difficult to use Minimax algorithms, and also to find a Nash equilibrium. Specific Monte Carlo methods enabled to get good playing programs in these games. However, till now, the best playing program for Phantom Go was a Flat Monte Carlo one, written in 2006 by Cazenave. As new methods have been found since then, we also tried a two-level variant of Monte Carlo, which would enable to take in consideration what does or does not know each player during the playout.